ADVANCED INTELLIGENT SYSTEMS

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Chapter 13

• ADVANCED INTELLIGENT SYSTEMS

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Learning Objectives

• Understand machine-learning concepts
• Learn the concepts and applications of case-based
  systems
• Understand the concepts and applications of
  genetic algorithms
• Understand fuzzy set theories and their
  applications in designing intelligent systems

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Learning Objectives

• Understand the concepts and applications of
  natural language processing (NLP)
• Learn the concepts, advantages, and limitations
  of voice technologies
• Learn about integrated intelligent support systems

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Machine-Learning Techniques

• Machine-learning concepts and definitions
• Machine learning
• The process by which a computer learns from
  experience (e.g., using programs that can learn
  from historical cases)

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Machine-Learning Techniques

• Human learning is a combination of many
  complicated cognitive processes including
• Induction
• Deduction
• Analogy
• Other special procedures related to observing or
  analyzing examples

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Machine-Learning Techniques

• How learning relates to intelligent systems
• Learning systems demonstrate interesting learning
  behaviors
• AI is not able to learn as well as humans or in
  the same way that humans
• Machine learning cannot be applied in a creative
  way, although such systems can handle cases to
  which they have never been exposed
• It is not clear why learning systems succeed or
  fail
• A common thread running through most AI
  approaches to learning is the manipulation of
  symbols rather than numeric information

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Machine-Learning Techniques

• Machine-learning methods
• Supervised learning
• A method of training artificial neural networks
  in which sample cases are shown to the network as
  input and the weights are adjusted to minimize
  the error in its outputs
• Unsupervised learning
• A method of training artificial neural networks
  in which only input stimuli are shown to the
  network, which is self-organizing

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Machine-Learning Techniques
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Machine-Learning Techniques
Machine-learning methods and algorithms

• Inductive learning
• Case-based reasoning
• Neural computing
• Genetic algorithms
• Natural language processing (NLP)
• Cluster analysis
• Statistical methods

• Explanation-based learning
• A machine learning approach that assumes that
  there is enough existing theory to rationalize
  why one instance is or is not a prototypical
  member of a class

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Case-Based Reasoning (CBR)

• Case-based reasoning (CBR)
• A methodology in which knowledge and/or
  inferences are derived from historical cases

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Case-Based Reasoning (CBR)

• Analogical reasoning
• Determining the outcome of a problem with the
  use of analogies. A procedure for drawing
  conclusions about a problem by using past
  experience
• Inductive learning
• A machine learning approach in which rules are
  inferred from facts or data

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Case-Based Reasoning (CBR)

• The basic idea and process of CBR
• Four-step process
• Retrieve
• Reuse
• Revise
• Retain

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Case-Based Reasoning (CBR)

• Definition and concepts of cases in CBR
• Ossified cases
• Cases that have been analyzed and have no
  further value
• Paradigmatic cases
• A case that is unique that can be maintained to
  derive new knowledge for the future

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Case-Based Reasoning (CBR)

• Definition and concepts of cases in CBR
• Stories
• Cases with rich information and episodes.
  Lessons may be derived from this kind of cases in
  a case base

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Case-Based Reasoning (CBR)
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Case-Based Reasoning (CBR)

• Benefits and usability of CBR
• CBR makes learning much easier and the
  recommendation more sensible

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Case-Based Reasoning (CBR)

• Advantages of using CBR
• Knowledge acquisition is improved.
• System development time is faster
• Existing data and knowledge are leveraged
• Complete formalized domain knowledge is not
  required
• Experts feel better discussing concrete cases
• Explanation becomes easier
• Acquisition of new cases is easy
• Learning can occur from both successes and
  failures

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Case-Based Reasoning (CBR)
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Case-Based Reasoning (CBR)

• Uses, issues, and applications of CBR
• Applications
• CBR in electronic commerce
• WWW and information search
• Planning and control
• Design
• Reuse
• Diagnosis
• Reasoning

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Case-Based Reasoning (CBR)

• Uses, issues, and applications of CBR
• Implementation issues for designers
• What makes up a case? How can we represent case
  memory?
• Automatic case-adaptation rules can be very
  complex
• How is memory organized? What are the indexing
  rules?
• The quality of the results is heavily dependent
  on the indexes used

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Case-Based Reasoning (CBR)

• Implementation issues for designers
• How does memory function in relevant information
  retrieval?
• How can we perform efficient searching (i.e.,
  knowledge navigation) of the cases?
• How can we organize the cases?
• How can we design the distributed storage of
  cases?
• How can we adapt old solutions to new problems?
  Can we simply adapt the memory for efficient
  querying, depending on context? What are the
  similarity metrics and the modification rules?

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Case-Based Reasoning (CBR)

• Implementation issues for designers
• How can we factor errors out of the original
  cases?
• How can we learn from mistakes? That is, how can
  we repair and update the case base?
• The case base may need to be expanded as the
  domain model evolves, yet much analysis of the
  domain may be postponed.
• How can we integrate CBR with other knowledge
  representations and inferencing mechanisms?
• Are there better pattern-matching methods than
  the ones we currently use?
• Are there alternative retrieval systems that
  match the CBR schema?

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Case-Based Reasoning (CBR)

• Success factors for CBR systems
• Determine specific business objectives
• Understand your end users and customers
• Design the system appropriately
• Plan an ongoing knowledge-management process
• Establish achievable returns on investment (ROI)
  and measurable metrics
• Plan and execute a customer-access strategy
• Expand knowledge generation and access across the
  enterprise

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Genetic Algorithm Fundamentals

• Genetic algorithms (GAs)
• Software programs that learn in an evolutionary
  manner similar to the way biological systems
  evolve

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Genetic Algorithm Fundamentals

• Genetic algorithm process and terminology
• Chromosome
• A candidate solution for a genetic algorithm
• Reproduction
• The creation of new generations of improved
  solutions with the use of a genetic algorithm

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Genetic Algorithm Fundamentals

• Genetic algorithm process and terminology
• Crossover
• The combining of parts of two superior solutions
  by a genetic algorithm in an attempt to produce
  an even better solution
• Mutation
• A genetic operator that causes a random change
  in a potential solution

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Genetic Algorithm Fundamentals
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Genetic Algorithm Fundamentals
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Genetic Algorithm Fundamentals

• A few parameters must be set for the genetic
  algorithm
• Number of initial solutions to generate
• Number of offspring to generate
• Number of parents and offspring to keep for the
  next generation
• Mutation probability (very low)
• Probability distribution of crossover point
  occurrence

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Genetic Algorithm Fundamentals

• Limitations of genetic algorithms
• Not all problems can be framed in the
  mathematical manner that genetic algorithms
  demand
• Development of a genetic algorithm and
  interpretation of the results requires an expert
  who has both the programming and
  statistical/mathematical skills demanded by the
  genetic algorithm technology in use
• In some situations, the genes from a few
  comparatively highly fit (but not optimal)
  individuals may come to dominate the population,
  causing it to converge on a local maximum

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Genetic Algorithm Fundamentals

• Limitations of genetic algorithms
• Most genetic algorithms rely on random number
  generators that produce different results each
  time the model runs
• Locating good variables that work for a
  particular problem is difficult
• Selecting methods by which to evolve the system
  requires thought and evaluation

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Developing Genetic Algorithm Applications

• GAs are a type of machine learning for
  representing and solving complex problems

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Developing Genetic Algorithm Applications
Applications of GAs include

• Dynamic process control
• Induction of optimization of rules
• Discovery of new connectivity topologies (e.g.,
  neural computing connections, i.e., neural
  network design)
• Simulation of biological models of behavior and
  evolution

• Complex design of engineering structures
• Pattern recognition
• Scheduling
• Transportation and routing
• Layout and circuit design
• Telecommunication
• Graph-based problems

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Fuzzy Logic Fundamentals

• Fuzzy logic
• Logically consistent ways of reasoning that can
  cope with uncertain or partial information
  characteristic of human thinking and many expert
  systems.
• Fuzzy sets
• A set theory approach in which set membership is
  less precise than having objects strictly in or
  out of the set

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Fuzzy Logic Fundamentals
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Fuzzy Logic Fundamentals

• Fuzzy logic applications in manufacturing and
  management
• Selection of stocks to purchase (e.g., the
  Japanese Nikkei stock exchange)
• Retrieval of data (because fuzzy logic can find
  data quickly)
• Inspection of beverage cans for printing defects
• Matching of golf clubs to customers swings
• Risk assessment
• Control of the amount of oxygen in cement kilns
• Accuracy and speed increases in industrial
  quality-control applications
• Sorting problems in multidimensional spaces

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Fuzzy Logic Fundamentals

• Fuzzy logic applications in manufacturing and
  management
• Enhancement of models involving queuing (i.e.,
  waiting lines)
• Managerial decision support applications
• Project selection
• Environmental control building
• Control of the motion of trains
• Paper mill automation
• Space shuttle vehicle orbiting
• Regulation of water temperature in shower heads

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Natural Language Processing (NLP)

• Natural language processing (NLP)  
• Using a natural language processor to interface
  with a computer-based system
• Two types of NLP
• Natural language understanding
• Natural language generation

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Natural Language Processing (NLP)

• Some problems that make NLP difficult
• Word boundary detection
• Word sense disambiguation
• Syntactic ambiguity
• Imperfect or irregular input
• Speech acts and plans

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Natural Language Processing (NLP)

• The current NLP technology
• Search and information retrieval
• A person enters a certain phrase, word, or
  sentence on which to search the Internet or some
  database, and NLP is then used to construct the
  best query possible

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Natural Language Processing (NLP)

• Applications of NLP
• Humancomputer interfaces
• Abstracting and summarizing text
• Analyzing grammar
• Understanding speech

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Natural Language Processing (NLP)

• Applications of NLP
• Front ends for other software packagesquerying a
  database that allows the user to operate the
  applications programs with everyday language
• Text mining
• FAQs and query answering

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Natural Language Processing (NLP)

• Machine translation
• Translation of content to other languages
• Criteria used to assess machine translation
• Intelligibility
• Accuracy
• Speed

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Voice Technologies

• Voice technologies fall into three broad
  categories
• Voice (or speech) recognition
• Voice (or speech) understanding
• Text-to-voice (or voice synthesis)

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Voice Technologies

• Voice (speech) recognition
• Translation of the human voice into individual
  words and sentences understandable by a computer
• Speech understanding
• An area of AI research that attempts to allow
  computers to recognize words or phrases of human
  speech

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Voice Technologies

• Advantages of voice technologies
• Ease of access
• Speed
• Manual freedom
• Remote access
• Accuracy
• Communicating while driving
• Quick selection
• Security
• Cost benefit

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Voice Technologies

• Limitations of speech recognition and
  understanding
• Inability to recognize long sentences, or the
  excessive length of time needed to accomplish
  that understanding
• High cost
• Speech may need to be combined with keyboard
  entry, which slows communication

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Voice Technologies

• Voice synthesis
• The technology by which computers convert
  text-to-voice (speak)
• A text-to-speech system is composed of two parts
  
• Front end takes input in the form of text and
  outputs a symbolic linguistic representation
• Back end takes the symbolic linguistic
  representation as input and outputs the
  synthesized speech waveform

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Voice Technologies

• Voice technology applications
• Call center
• Contact of customer care center
• Computer/telephone integration (CTI)
• Interactive voice response (IVR)
• Voice portal
• Voice over IP (VoIP)

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Voice Technologies

• Voice portals
• Web sites, usually portals, with audio interfaces

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Developing Integrated Advanced Systems

• Fuzzy neural networks
• Fuzzification
• A process that converts an accurate number into
  a fuzzy description, such as converting from an
  exact age into young or old
• Defuzzification
• Creating a crisp solution from a fuzzy logic
  solution

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Developing Integrated Advanced Systems
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Developing Integrated Advanced Systems
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Developing Integrated Advanced Systems

• Genetic algorithms and neural networks
• The genetic learning method can perform rule
  discovery in large databases, with the rules fed
  into a conventional ES or some other intelligent
  system
• To integrate genetic algorithms with neural
  network models use a genetic algorithm to search
  for potential weights associated with network
  connections
• A good genetic learning method can significantly
  reduce the time and effort needed to find the
  optimal neural network model